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@SIMUL8 Virtual User Group, September: Brian Harrington, Less is More

2.
Chaos of Change
We need to batch
build the Bodyside
Outers.
The new laser
welder looks like it
will be over 58
seconds.
How many carriers
are on the FS
delivery system?
We will be running
this 2-10 hr shifts.
How many units
between Seg1 and
Seg2?
The Main
Framing
conveyor runs
at 45fpm.
There are 3 repair
resources available.
We have 3 different
Pallet types. What
is the build
schedule?
Last week… we had
a 3 hr breakdown.
Looks like we are
2 weeks behind!
3 seconds for the
lift table to go up,
then another 3
seconds to drop.
2

3.
Complex Systems and Shorten
Development Time
This is a good equation for stress!
We’ll simulate it!
This just pushed the stress on the simulation team!
3

6.
The Art & Science of DES
Experience modelers tend to create a model with the least amount
of objects (data) to meet the objective.
A common modeling error is to add too much detail to the model;
attempting to simulate every move or event within the system.
The two key questions become:
1. What is the significant input?
2. How do we control it?
6

7.
The P-Diagram
The P-Diagram not only helps engineers to define the Key Parameters for
a robust design, but also acts as an excellent communication tool for
team reviews.
7

10.
Manufacturing Example
Problem Statement: 30% proposed increase in throughput of an overhead
sequencing bank of finite capacity
Questions from management:
Do we need to add capacity (additional lane), or
can we maintain the current size and improve
our sequencing routing logic?
The two key questions from Simulation Team:
1. What is the significant input?
2. How do we control it?
10

12.
Reduce Inputs & Get Answers
1. Examine the system assuming ideal (perfect)
sequence logic.
2. Some of the N’s & X’s can be ignored.
3. Determine the breakpoint where the system fails;
maybe it’s 23%.
 Use the least amount of data to get initial answers
to management ASAP!
12

13.
Establish Credibility
1. The program team might have critical answers sooner;
hence managers have additional time to secure funds.
2. The initial answers provided and backed by data will keep
the team engaged, and eager to provide further details!
3. The simulation engineer learns more about the system,
and can add additional details as necessary.
13

14.
Graph your Data!
One of the most basic steps in 6-Sigma; Exploit your data!
Stat-Fit for
SIMUL8
14

15.
Leverage Statistical Distributions!
• Curve fit your data! Instead of using lengthy
spreadsheets.
• Black-box; entire segments of the model can be
collapsed using distributions.
• If using empirical datasets, drop them into a “S8
Probability Profile Distribution”
15

16.
Use Known Distributions
The data collection phase of modeling can be the
lengthiest and most time consuming.
i.e.)
Downtime (MTBF & MTTR); such as Exponential & Erlang
respectively.
Cycle times often use a Fixed distribution; that is the “Design
Cycle Time”.
16

17.
Steady State
A common data collection error is to capture all data
points, and attempt to force them into one
distribution.
– Filter out the outliers; usually catastrophic points are
outside the scope of the steady state system.
17

18.
Tornado of Change
Utilizing Six-Sigma tools in conjunction with model building
keeps the team informed on what is of most importance to get
the program launched.
This is how several large manufacturing companies are able to
shorten the overall launch time, and bring their new product to
the market before the competition
If you find yourself caught up in the tornado of
change; just remember “Less is More”!
18